Tag Archives: modeling

Advanced power system modeling need not mean more complex modeling

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A recent article by E3 and Form Energy in Utility Dive calls for more granular temporal modeling of the electric power system to better capture the constraints of a fully-renewable portfolio and the requirements for supporting technologies such as storage. The authors have identified the correct problem–most current models use a “typical week” of loads that are an average of historic conditions and probabilistic representations of unit availability. This approach fails to capture the “tail” conditions where renewables and currently available storage are likely to be sufficient.

But the answer is not a full blown hour by hour model of the entire year with many permutations of the many possibilities. These system production simulation models already take too long to run a single scenario due to the complexity of this giant “transmission machine.” Adding the required uncertainty will cause these models to run “in real time” as some modelers describe it.

Instead a separate analysis should first identify the conditions under which renewables + current technology storage are unlikely to meet demand sufficiently. These include drought that limits hydropower, extreme weather, and extended weather that limits renewable production. Then these conditions can input into the current models to assess how the system responds.

The two important fixes which has always been problem in these models are to energy-limited resources and unit commitment algorithms. Both of these are complex problems, and these models have not done well in scheduling seasonal hydropower pondage storage and in deciding which units to commit to meet a high demand several days ahead. (And these problems are also why relying solely on hourly bulk power pricing doesn’t give an accurate measure of the true market value of a resource.) But focusing on these two problems is much easier than trying to incorporating the full range of uncertainty for all 8,760 hours for at least a decade into the future.

We should not confuse precision with accuracy. The current models can be quite precise on specific metrics such as unit efficiency as different load points, but they can be inaccurate because they don’t capture the effect of load and fuel price variations. We should not be trying to achieve spurious precision through more complete granular modeling–we should be focusing on accuracy in the narrow situations that matter.

How to choose a water system model

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The California Water & Environmental Modeling Forum (CWEMF) has proposed to update its water modeling protocol guidance, last issued in 2000. This modeling protocol applies to many other settings, including electricity production and planning (which I am familiar with). I led the review of electricity system simulation models for the California Energy Commission, and asked many of these questions then.

Questions that should be addressed in water system modeling include:

  • Models can be used for either short-term operational or long term planning purposes—models rarely can serve both masters. The model should be chosen for its analytic focus is on predicting with accuracy and/or precision a particular outcome (usually for short term operations) or identifying resilience and sustainability.
  • There can be a trade off between accuracy and precision. And focusing overly so on precision in one aspect of a model is unlikely to improve the overall accuracy of the model due to the lack of precision elsewhere. In addition, increased precision also increases processing time, thus slowing output and flexibility.
  • A model should be able to produce multiple outcomes quickly as a “scenario generator” for analyzing uncertainty, risk and vulnerability. The model should be tested for accuracy when relaxing key constraints that increase processing time. For example, in an electricity production model, relaxing the unit commitment algorithm increased processing speed twelve fold while losing only 7 percent in accuracy, mostly in the extreme tail cases.
  • Water models should be able to use different water condition sequences rather than relying on historic traces. In the latter case, models may operate as though the future is known with certainty.
  • Water management models should include the full set of opportunity costs for water supply, power generation, flood protection and groundwater pumping. This implies that some type of linkage should exist between these types of models.